Using eigenvalues as variance priors in the prediction of genomic breeding values by principal component analysis
Using eigenvalues as variance priors in the prediction of genomic breeding values by principal component analysis
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DOI：
10.3168/jds.2009-3029
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发表时间：
2010-06-01
影响因子：
3.5
通讯作者：
Cappio-Borlino, A.
中科院分区：
文献类型：
--
作者：
Macciotta, N. P. P.;Gaspa, G.;Cappio-Borlino, A.
全基因组选择旨在通过使用密集单核苷酸多态性 (SNP) 标记图谱估计染色体片段对表型的影响来预测个体的遗传价值。在本文中，主成分分析用于减少模拟群体基因组育种值估计中的预测变量数量。主成分提取要么使用所有可用的标记进行，要么单独针对每条染色体进行。预测变量方差的先验基于它们对总 SNP 相关结构的贡献。主成分方法与直接使用 SNP 基因型进行回归获得的预测基因组育种值的准确性相同，预测变量数量减少了约 96%，计算时间减少了 99%。虽然这些精度低于目前使用贝叶斯方法实现的精度，但至少对于模拟数据而言，计算速度的提高以及直接在单个染色体上提取主成分的可能性可能代表了在具有大量 SNP 的真实数据中预测基因组育种值的一个有趣的选择。使用表型作为因变量而不是传统的育种值可以得到更可靠的估计，从而支持当前牲畜基因组选择研究计划中采用的策略。
Genome-wide selection aims to predict genetic merit of individuals by estimating the effect of chromosome segments on phenotypes using dense single nucleotide polymorphism (SNP) marker maps. In the present paper, principal component analysis was used to reduce the number of predictors in the estimation of genomic breeding values for a simulated population. Principal component extraction was carried out either using all markers available or separately for each chromosome. Priors of predictor variance were based on their contribution to the total SNP correlation structure. The principal component approach yielded the same accuracy of predicted genomic breeding values obtained with the regression using SNP genotypes directly, with a reduction in the number of predictors of about 96% and computation time of 99%. Although these accuracies are lower than those currently achieved with Bayesian methods, at least for simulated data, the improved calculation speed together with the possibility of extracting principal components directly on individual chromosomes may represent an interesting option for predicting genomic breeding values in real data with a large number of SNP. The use of phenotypes as dependent variable instead of conventional breeding values resulted in more reliable estimates, thus supporting the current strategies adopted in research programs of genomic selection in livestock.